Construction of Sukkur (Lloyd) Barrage and Canal, Sukkur, … | Flickr M Bhatia By: M Bhatia Construction of Sukkur (Lloyd) Barrage and Canal, Sukkur, Sind, 1924 Assembly of the Ruston3000 steam excavator on site, at Sukkur, Sind , in 1924 during the Lloyd Barrage and Canal Construction Scheme project. From the book " Lincoln's excavators: The Ruston Years 1875-1930" by Peter Robinson. There were six of these machines delivered to this scheme in 1924. From the book: "Commenced in 1923 and officially known as the Lloyd Barrage and Canal Construction Scheme this massive project, the purpose of which was to irrigate 6 1/2 million acres of land in the Sind desert of NW India, involved the excavation of more than 210 million cubic yards of material and the construction of the Sukkur Dam nearly a mile long spanning the river Indus.

It was the largest irrigation scheme in the world, with a cost greater than the Suez Canal and involving four times as much excavation." There are seven main canals having a total lenght of 1,000 miles and ranging from 79ft to 346ft in width and up to 20ft in depth. Also 700 miles of branch canals and 4,000 miles of distributory canals, plus 50,000 miles of final feeders excavated by the farmers who rented the land. There were fierce winds and sandstorms almost every day, nowhere to service machinery, no water suitable for boilers, few roads, and it was necessary to build tramways to supply the machines with coal, oil etc. Temperature was 49*C (120*F) in the shade at times! Camels used for supply in outlying areas, and men lived in tents near the machines. Three shifts were operated per day, all year, mostly operated by Indians. The whole scheme was completed in 1932. Done Sind Indus River Rajasthan Khairpur Hydrabad Sukkur Dam Pakistan takes its name after its river "Sindhu" ( Indus ).

Its aridity and the absence of monsoons makes its climate rank among the hottest and most variable. In size it is the third biggest province (1,40,915 kms); north to south it is 560 and east to west about 464 kms. wide. River Indus dominates the life and economy of Sind . The plain from north to south slopes at an average of about 2.5m inches per kilometre. Thus, its own bed is often above the general level of the plain needing embankments against flooks. Its capital, Karachi , is country's biggest city. The father of the nation, Quaid-e-Azam Muhammad Ali Jinnah was born here (1876) and his mausoleum marks prominently the sky line of the metropolis. Boat trip on River Indus at Sukkur - YouTube Boat trip on River Indus at Sukkur Want to watch this again later? Sign in to add this video to a playlist. Need to report the video? Sign in to report inappropriate content. Rating is available when the video has been rented. This feature is not available right now. Please try again later.

Published on Jul 23, 2015 Boat-trip on river Indus from Lab-e-Mehran (a recreation park on the bank of Indus) at Sukkur to Rohri, Sindh, Pakistan and back, January 2015. Category Gudu Barrage | dam, Pakistan | Britannica.com Gudu Barrage THIS IS A DIRECTORY PAGE. Britannica does not currently have an article on this topic. Alternative Title: Guddu Barrage Learn about this topic in these articles: in Indus River: Irrigation ...and Muzaffargarh districts, also produces about 100,000 kilowatts of electricity. Within the Sindh there are three major barrages on the Indus—Guddu, Sukkur, and Kotri, or Ghulam Muhammad. The Guddu Barrage is just inside the Sindh border and is some 4,450 feet (1,356 metres) long; it irrigates cultivated land in the region of Sukkur, Jacobabad, and parts of Larkana and Kalat districts....

Dam water - The Express Tribune The Express Tribune Share Tweet Email LAHORE: It is not only the average man on the street, but also many educated people in Sindh who blame dams for causing depletion in the Indus River and triggering the degradation of the Indus Delta. However, the record in the Sindh irrigation department will show that water available to the Sindh canals have increased from the pre-Mangla 35.6 million acre feet (maf) level to the post-Tarbela 44.5maf — in other words, an additional 8.9maf. Now, if this additional water did not reach the delta, then where did it go? All of the 44.5maf of water is being sucked out of the river by the 14 canals at Guddu, Sukkur and Kotri barrages, leaving very little to flow below Kotri, thereby causing degradation of the delta. But why do provinces still complain of shortages? Because the population — and hence, the demand — has increased from eight crore to 20 crore people over the last 40 years when the last addition to supplies was made.

It is the ignorance on the part of politicians and even the elite, who are holding back development in the country. The media must also share responsibility for allowing this criminal ignorance to persist. Engr Khurshid Anwer Published in The Express Tribune, December 31st, 2015. Like Opinion & Editorial on Facebook , follow @ETOpEd on Twitter to receive all updates on all our daily pieces. Pakistan Emergency Situational Analysis - District Sukkur, September 2014 - Pakistan | ReliefWeb Pakistan Emergency Situational Analysis - District Sukkur, September 2014 Report 1.1. Introduction 1.1.1. History District Sukkur has been an important administrative unit that played an important role in the economy, trade and history of upper Sindh. Historically, Alexander invaded India in 326 BCE and the ruins of Greek invasion can still be seen in this district. The Arab invasion, led by Muhammad Bin Qasim, in 711 A.D, made Sindh part of the Umayyad Caliphate.

Later on, this region was ruled by different dynasties, including the Soomras (1024-1351, the Arghuns (1520-1650), the Kalhoras (1657-1783) and the Talpurs (1783-1843). When Britain invaded the subcontinent, General Charles Napier, a commander in the British Army, defeated the Talpur dynasty and conquered Sindh in 18431. There are different historical sources pertaining to the origin of name “Sukkur”. Some historians believe that Mohammad Bin Qasim, after passing through a thick forest, reached this hilly plain and uttered the word “Shukr” meaning thanks in Arabic, which was in time transformed into Sukkur. Some are of the view that the word has been taken from “Sukh”, meaning happiness. It is further argued that there was cultivation of sugarcane surrounding the place and it was therefore named “Shakker”, meaning sugar. Some are of the opinion that it is the degenerated form of Bakhar and B was changed into S.

Another historian states that the old town of Sukkur was said to have been founded about 375 A.D by a Hindu Brahmin named Verbham (Birmanji) and was named after him. In 492 A.D, its name was changed to “Shukar” to perpetuate the memory of Birmanji’s death, which took place on a Friday, “Shukar” in Sanskrit. The name was gradually corrupted to Sukkur2 . Hence, there are many historical references about the name Sukkur and the authenticity of any of these is still unclear. The Bristish contributed a lot towards the development of Sukkur. General Charles Napier was appointed as the first Governor General of Sindh. The province was divided into different administrative units and assigned to Zamindars (landlords) to collect taxes for the British government. The British government developed these areas as urban centers. Consequently, People migrated from other districts and provinces as well and started to reside here. The British named these small developed areas as "Talukas". They built a network of roads, schools, dispensaries and many other civic amenities throughout the province.

District Sukkur that was previously part of of Shikarpur district was constituted in 1901. Sukkur saw a significant socio-economic uplift after 1932, when the British government built Sukkur Barrage, on Indus River. 1.1.2. Geography District Sukkur lies in 680 35” 30’ to 690 48” 0’ east longitudes and 270 04” 0’ to 280 02” 15’ north latitudes. This district is bounded by district Ghotki and India on the east, district Kashmore on the north, district Shikarpur on the north-west, and district Khairpur on the west and south. Indus River flows on the north-western side of this district. Sukkur is the narrowest part of the Indus River course. The land cover structure of this district comprises of the irrigated croplands on the western and northern side and barren areas in the east. On the western and northern side of the district, due to Indus River, the plane lands are fertile and are ideal for cultivation.

But on the eastern side, large tracks of barren lands are prevalent, particularly in the union councils of Tarai and Lal Juryo Khan Shambani. On the southern part of this district, both vegetation and barren lands can be seen. The climate of this district is hot during summer while dry and cold in winter. During January, the temperature ranges from 7 °C to 22 °C. The summer temperature averages 35 °C though it often reaches up to 52°C. Generally, the summer season commences in March - April and ends before October. The average rainfall of the district is 88 mm, per annum (with monthly ranges ranges from 0.59 mm to 25.62 mm). Ongoing 3rd Yousufzai M A K Abstract Annual flood peak discharges is widely used in risk assessment. Major sources of flooding in Pakistan are River Jhelum, River Chenab, River Kabul, and upper and lower parts of River Indus. These rivers are major tributaries of the River Indus System which is one of the most important systems of the world and the greatest system of Pakistan.

River Indus is the longest river of Pakistan containing seven gauge stations and several barrages, and it plays a vital role in the irrigation system and power generation for the country. This paper estimates the risk of flood in River Indus using historical data of maximum peak discharges. On the basis of our analysis, we find out which dam/barrage reservoir need to be updated in capacity, and whether there are more dams/barrages needed. Discover the world's research Flood risk assessment of River Indus of Pakistan Bushra Khan &Muhammad Jawed Iqbal & M. Ayub Khan Yosufzai Received: 6 June 2009 / Accepted: 23 August 2009 / Published online: 5 January 2010 #Saudi Society for Geosciences 2009 Abstract Annual flood peak discharges is widely used in risk assessment. Major sources of flooding in Pakistan are River Jhelum, River Chenab, River Kabul, and upper and lower parts of River Indus. These rivers are major tributaries of the River Indus System which is one of the most important systems of the world and the greatest system of Pakistan.

River Indus is the longest river of Pakistan containing seven gauge stations and several barrages, and it plays a vital role in the irrigation system and power generation for the country. This paper estimates the risk of flood in River Indus using historical data of maximum peak discharges. On the basis of our analysis, we find out which dam/barrage reservoir need to be updated in capacity, and whether there are more dams/barrages needed. Keywords Annual flood peak discharges .Risk assessment . River Indus Life loss and capital damages are of major concerns in human societies. One of the major causes to such phenomena is floods which occur in almost all the South Asian countries. Especially, over the past 25 or 30 years, the frequency and magnitude of disastrous floods is increased due to the impacts of climate changes. Now, risk assessment is defined as scientific quantification of hazard from data to understand the procedures involved in (Coburn and Spence 1994). Incompatibility between hazard and vulnerability is the main cause of risk (Ologunorisa and Abawua 2005). In the context of risk, some closely related terms are also used, viz., hazard and vulnerability.

How- ever, according to the United Nation Commission for Human Settlements (UNCHS-HABITAT 1981), there are clear distinctions among the meanings of these terms. They define that risk can be directly related to the perception of disaster, given that it incorporates total losses and harm that can undergo after a natural hazard. Risk involves a future potential condition, a function of the magnitude of natural hazard and the vulnerability of all exposed elements in a determined moment, whereas, hazard is defined as the probability that, in a given period and a given area, a catastrophic damaging natural phenomenon occurs. Flood risk is measured in terms of probability of occurrence of events and the related consequences (Smith 1996). This means that risk and improbability in water resources take place from the natural inconsistency of geophysical progressions and alterations in difficult socio- economic features. As a result, risk analysis is performed on the measurement of the probabilities of occurrence of flood and their likely consequences (Kaczmarek 2003). There are various techniques for assessment of flood risk, such as assessing meteorological parameters, hydrological B.

Khan Department of Computer Science and Engineering, Bahria University (Karachi Campus), Department of Mathematics and Institute of Space and Planetary Astrophysics, University of Karachi, Karachi, Pakistan Department of Applied Physics and Institute of Space and Planetary Astrophysics, University of Karachi, Karachi, Pakistan Arab J Geosci (2011) 4:115–122 DOI 10.1007/s12517-009-0110-9 parameters, socioeconomic factors, and combination of hydrometeorological and socioeconomic factors along with assessment based on geographical information system as explained in (Ologunorisa and Abawua 2005). The authors state that meteorological parameters have been widely used in most countries, such as Malaysia, Korea, USA, Aus- tralia, Ethiopia, and Israel including Pakistan. Risk is also assessed via annual flood peak discharge (Kattlemann 1997), the technique is now termed as assessment of flood through hydrological parameters, and the key factor is rainfall on snow-covered catchments during warm storms.

He also notices that rivers of Sierra Nevada, California, experience most destructive floods all through warm storms when rain waters falls in snow-covered catchments of the river. Oriola (1994) noticed that many of the Nigerian urban surroundings flooding are caused by the violation of the river canal. The author attributes illiteracy, ununiformity, deprived ecological culture and supervision, inefficient municipality laws, and community unawareness as major socioeconomic factors contributing in flood risk. He furthers the insights in flood risk by showing that flood risk in Nigeria acts as a function of not only the above characteristics but the amount and duration of precipitation, slope of river basin, and some other parameters of river basin. Combination of hydrometeorological and socioeco- nomic factors is explained (Hogue et al. 1997). They indentify the hazard risk in Chittagong by predicting the probability of occurrence of the storm flooding of different extents and depths. They divide the city into five major sectors whereas the residents and the economic importance of that sector are considered as the importance indices while the sectors are trading or industrial sector, deliberated housing sector, business or commercial sector, ingenuous housing sector, and combined or mixed sectors.

Laughlin and Kalma (1990) built up a technique for flood risk mapping based on regional weather data and local territory. This study demonstrates the regional weather and the territory’s effect with 3D block diagram. Hayden (1988) noticed that the existing literature based on flood risk assessment does not make a general nature of flooding; therefore, author delineates the climate regions on global scale by using the basis of meteorological parameters. McEwen (1989) compares the rain fall pattern with the published study of other long-standing rainfall proceed- ings to assess the local deviationinthenatureofsevere rainfall. It is further discussed that flood risk can also be assessed by the probability of embankment or dam failure (Van et al. 2005). The Indian Meteorological Department (1971) classifies the seasonal rainfall as the flood of less intensity if it lies in 0–26% of normal, the moderate flood if it is between 26% and 50% of normal, and the flood of high intensity if the flood is above 50% of the normal.

Geographical Information system (GIS) is another very successful tool to assess the flood risk in the flood-prone areas. Recently, GIS technique is shown to be able to unite all the known procedures and factors for predicting flood risk (Ologunorisa and Abawua 2005). In this study, it is observed that GIS technique is more powerful technique to assess the flood risk. As for Pakistan, this technique is used to assess flood risk in flood-prone areas near River Indus (Khan 2007). The author uses remote sensing, geographical information system, and digital image processing to assess the flood risk in River Indus. The study integrates the remote-sensing techniques with geographic information system using satellite data and gives a solution to reduce flood risk by constructing some new dams on the river. Tahirkaili and Nawaz (2003) and Nawaz and Shafique (2003) also use the same methodology to assess the flood risk in the river Jhelum, Pakistan. This study examines flood risk of river Indus in Pakistan. We use flood peak discharge data from 1942 to 2008 of the River Indus to assess risk.

The data set, obtained from Federal Flood Commission, Islamabad, comprise less than 30 recorded values at seven gauge stations. In “Estimation of probable maximum flood” section, we describe our calculations regarding modeling of the data using normal, log-normal, and Weibull distribution on both the observed and simulated data models. Here, we also compute return period (T) and exceedence probability (P) of the flood of different intensities for the period from 1992 to 2008. The “Flood risk assessment of River Indus”section is used to describe our results obtained after applying log-Pearson type III (LP3) analysis in order to forecast flood peak discharge values with specified Tfor the same period. “Summary and discussion”section is opens with discussion and summa- rizes the conclusion based on our findings. Estimation of probable maximum flood Pakistan faces large amounts of frequent river flooding every year due mainly to monsoon rains and melting snow which push the rivers of Pakistan over their banks. The major rivers which cause flooding in the country are the River Jhelum and the River Chenab in Punjab province and lower part of River Indus in Sindh province, see Fig. 1).

Besides, hill torrents also take some part in flooding Sindh. This sort of flooding also affects the hilly areas of North- West Frontier Province (NWFP), Baluchistan, and northern areas of Pakistan. Districts of Charsadda, Noshera, and Peshawar in NWFP are exposed to risks from flooding in River Kabul. Large cities of Pakistan like Karachi, Lahore, and Rawalpindi have experienced flooding due to the failure of sewerage system to cope up with intense 116 Arab J Geosci (2011) 4:115–122 precipitation. In the present work, assessment of flood risk on the river Indus is attempted. This river, i.e., the River Indus is a great trans-Himalayan river and one of the longest rivers in the world. It is the longest river in Pakistan containing seven dams/barrages and having a length of 1,800 miles. It has a total drainage area of about 450,000 square miles of which 175,000 square miles lie in mountains and foothills and the rest lie in the semiarid plains of Pakistan.

The annual rainfall in the Indus region varies between 125 to 500 mm and excluding the mountainous regions of the country, the Indus valley lies in the driest part of the subcontinent (Britannica). River has seven gauge stations to monitor its flow continuously throughout the year. Likewise, it has seven dams/barrages namely Tarbela Dam, Kalabagh or Jinnah Barrage, Chashma Barrage, Taunsa Barrage, Guddu Barrage, Sukkur Barrage, and Kotri Barrage, respectively. We obtain uneven data of annual peak discharge of River Indus from 1942 to 1992 and evenly distributed yearly data from 1992 to 2008.

According to Federal Flood Commis- sion, Islamabad, classification of flood in Pakistan is defined 250,000–300,000 cusecs per cubic feet per second (cfs) as low flood, 300,000–450,000 cfs as medium flood, 450,000–650,000 cfs as high flood, 650,000–800,000 cfs as very high flood, and 800,000 cfs to onward as extremely very high flood (Annual Flood Report 2006). First, we compute probable maximum flood (PMF) from observed values. The values of PMF are shown in Table 1for normal, Fig. 1 Location map of the River Indus in Pakistan Arab J Geosci (2011) 4:115–122 117 log-normal, and Weibull distributions. The Anderson– Darling (AD) test results show that Jinnah Barrage cannot be modeled by normal distribution.

Similarly, Taunsa, Sukkur, and Kotri Barrages do not follow log-normal distribution. However, it is quite clear that data of all dams/ barrages follows Weibull distribution. We next perform simulation on observed data by employing normal, log-normal, and Weibull distributions and generate samples of sizes 100 for each of the seven dams/barrages of the river Indus (viz., Tarbela Dam, Kalabagh or Jinnah Barrage, Chashma Barrage, Taunsa Barrage, Guddu Barrage, Sukkur Barrage, and Kotri Barrage, respectively). Each of these samples is used to compute PMF of hundred years for each dam. The results are summarized in Table 2. The AD test results for this also show that Jinnah Barrage cannot be modeled by normal distribution whereas Taunsa, Sukkur, and Kotri Barrage do not follow log-normal distribution. Similarly, Weibull distribution again is found to fit in all the cases considered in the present study. The use of simulation proves to be very useful as it gives us estimate of PMF for hundred years.

This means that classification among various gauge stations of the River Indus can now be performed by finding where the values of PMF (Table 2) lie in the ranges as defined by Federal Flood Commission, Islamabad as mentioned above. For instance, one can easily see that Tarbela Dam and Kalabagh are on the risk of medium flood, Chashma and Taunsa Barrage are on the risk of high flood, Guddu and Kotri Barrage are on the risk of high to very high flood, and Sukkur Barrage is on the risk of very high flood. Let us now compute the return period using the following Weibull formula: where Tdenotes the return period or recurrence interval, and Pstands for exceedence probability, P¼1=Tð2Þ of low, moderate, high, very high, and extremely very high flood, where nis the number of annual flood peak discharge, mshows the rank of flood; the highest peak has m=1, second highest peak has m=2, and so on (Kiely 1998).

We summarize our calculation in Table 3which manifests this procedure for Tarbela Dam; flood of 1992 has return period of 18 years with 5% exceedence probability, flood of 1995 has return period of 9 years with 10% exceedence probability, flood of 1997 has return Gauge stations Normal Log-normal Weibull PMF (cfs) AD statistic PMF (cfs) AD statistic PMF (cfs) AD statistic Tarbela 369 0.216 368 0.258 369 0.250 Jinnah –– 517 0.255 514 0.665 Chashma 485 0.223 489 0.544 485 0.209 Taunsa 478 0.520 –– 479 0.522 Guddu 630 0.508 657 0.520 632 0.456 Sukkur

 648 0.628 –– 645 0.810 Kotri 473 0.531 –– 470 0.435 ×1,000 except for AD test results Table 1 Estimation of PMF using normal, log-normal, and data of flood peak discharge from 1942 to 2008 (significance level used is α=0.05) Gauge station Normal Log-normal Weibull PMF (cfs) AD statistic PMF (cfs) AD statistic PMF (cfs) AD statistic Tarbela 360 0.729 362 0.273 362 0.219 Jinnah –– 518 0.235 524 0.375 Chashma 478 0.6 458 0.431 470 0.213 Taunsa 476 0.263 –– 471 0.288 Guddu 588 

0.302 703 0.159 671 0.396 Sukkur 663 0.563 –– 650 0.266 Kotri 480 0.280 –– 471 0.144 ×1,000 except for AD test Table 2 Estimation of PMF of simulated data of flood peak discharge from 1942 to 2008 (significance level used is 118 Arab J Geosci (2011) 4:115–122 period of 6 years with 16.6% exceedence probability, and finally, we see that flood of 2001 has return period of 1 year with 94% exceedence probability, which shows that flood of larger intensities have low probability of occurrence and longer recurrence interval whereas flood of smaller inten- sities occurs more frequently with short return period.

We find similar results for other gauge sites of River Indus. Flood risk assessment of River Indus This section applies log-Pearson type III analysis for flood risk assessment. For this analysis, the annual flood events from 1992 to 2008 are presumed to be random variables observing log-Pearson type III probability distribution. The analysis is performed in conjunction with the historic record adjustments (Flynn et al. 2006). If Xshows the ordinary logarithm of the peak discharge data, Sis the standard deviation of X,Xis the mean of X,K N significance level critical value for outlier test statistic for samples of size Nfrom normal distribution, then XH¼XþKNSð3Þ is the logarithmic higher outlier test threshold, and XL¼XKNSð4Þ is the logarithmic low outlier test threshold.

The historic record adjustment is applied by computing W¼HNHP NHO ðÞ=NsNHO to mitigate the historic peaks and high outliers, where Wis the weight applied to below threshold systematic peak, N s is the number of systematic peaks (peaks below high outlier threshold and above base peaks), N HP is the number of high outliers, and Hshows the time period.

The number of peaks above the flood base is N¼HWNBB with N BB being the number of peaks lower than the flood base Year MPD (cfs) Log peak discharge Xm T P 1992 500,000 5.69897 1 18 0.05 1995 480,000 5.681241 2 9 0.1 1997 450,000 5.653213 3 6 0.166 1994 420,000 5.623249 4 4.5 0.2 1996 402,000 5.604226 5 3.6 0.27 2005 372,900 5.571592 6 3 0.33 1999 372,000 5.5705

43 7 2.5 0.4 2006 371,800 5.570309 8 2.25 0.44 1993 370,000 5.568202 9 2 0.5 2003 350,000 5.544068 10 1.8 0.55 1998 347,700 5.541205 11 1.6 0.625 2007 292,600 5.466274 12 1.5 0.666 2002 290,900 5.463744 13 1.38 0.72 2004 269,900 5.431203 14 1.28 0.78 2000 

243,600 5.386677 15 1.2 0.83 2008 237,000 5.374748 16 1.125 0.88 2001 229,900 5.361539 17 1.0588 0.94 Table 3 Annual MPD data in cusecs from 1992 to 2008 versus return period Tand exceedence where X¶and X¶¶ both are ordinary logarithm of systematic peaks and historic maximum peaks plus high outliers, respectively.

The risk of flood, Qin cfs, is described here as under: where Kis the frequency factor (see frequency factors K for gamma and LP3 distributions (Haan 1977)). If there are no peaks above the high outlier threshold, i.e., any historic peak and high outlier (see Taunsa and Kotri in Tab le 4), then LP3 Analysis without historic record adjustment is used (Hydraulic Design Manual 2004)in which peaks below the lower outer threshold is excluded such that log Q¼XþSK ð11Þ where Qis the risk of flood in cfs, Xis the mean of logarithmic annual peaks, and Sis the standard deviation of logarithmic annual peak discharges. Now, by using Eq. 6, we calculate probability of flood exceeding flood base (P ) which equals to 1 because the annual flood peak discharge record of River Indus is concern, no zero or low magnitude peak is obtained in last 17 years, likewise weighted mean M  by using Eq. 8, and weighted skewness G by using Eq.

9of systematic peaks for Tarbela Dam, Kalabagh, Chashma Barrage, Sukkur Bar- rage, and Guddu Barrage along with logarithmic high outlier threshold (X ) and logarithmic low outlier threshold (X ; see Table 4). Later than using Eq. 10, we calculate the risk of flooding in the aforesaid dams/barrages where as Eq. 11 is used to calculate flood risk in Taunsa and Kotri. Table 5shows these calculations for the gauge sites Tarbela and Taunsa by using the parameters of Table 4with specified return period while the remaining gauge stations of River Indus also experience the same procedure for calculating the flood risk.

Table 5 Flood risk calculation in Tarbela Dam and Taunsa Barrage by using peak flow data from 1992 to 2008 Number (T) Tarbela Dam Taunsa Barrage (K)Q cfsðÞ (K)Q(cfs) 1 2 0.000 343,510 0.017 4,044,867 2 5 0.842 433,491 0.846 561,680 3 10 1.282 489,531 1.27 664,056 4 25 1.751 557,263 1.716 791,943 5 50 2.054 605,927 2.000 885,931 6 100 2.326 653,221 2.252 978

,628.8 7 200 2.576 699,939 2.482 1,071,674.7 Table 6 Design/spillway capacity of reservoirs are shown with maximum peak discharge data according to Federal Flood Commission, Islamabad classification with return periods Gauge sites Design/spillway capacity of reservoir (cfs) Extremely very high flood Very high flood High flood MPD TMPD TMPD T Jinnah 950,000 849,245 18 654,200 9 551,553 6 Taunsa 950,000 ––655,079 18 612,300 9 Chashma 1,100,000 ––668,336 18 635,400 9 Guddu 1,200,000 1,086,919 18 790,163 4.5 626,4

10 3 Sukkur 1,500,000 1,068,072 18 757,390 4.5 572,500 3 Kotri 850,000 826,369 18 799,440 9 –– 120 Arab J Geosci (2011) 4:115–122 Summary and discussion The values of PMF as obtained through observed and simulated data shows similar results, as discussed in “Estima- tion of probable maximum flood”section.

This also means that our results of simulation are found to be consistent with those obtained by using observed data. Also, the use of simulation proves to be very useful as it gives us estimate of PMF for hundred years. This means that classification among various gauge stations of the River Indus can now be performed by finding where the values of PMF (Table 2) lie in the ranges as defined by Federal Flood Commission, Islamabad and mentioned in “Estimation of probable maximum flood”section. For instance, one can easily see that Tarbela Dam and Kalabagh are on the risk of medium flood, Chashma and Taunsa Barrage are on the risk of high flood, Guddu and Kotri Barrage are on the risk of high to very high flood, and Sukkur Barrage is on the risk of very high flood. To the best of our knowledge, such a classification is done the first time on the River Indus, using probabilistic approach. However, Khan (2007)performs similar analysis through GIS technique. Also, the results of Weibull formula show that the small floods are more likely to occur than large floods.

These results are also confirmed by LP3 analysis (see Table 4) which is also used here to forecast flood risk. Moreover, our analysis suggests (see Tab le 6) that, except Tarbela Dam, there is an urgent need to construct new dams/barrages on the River Indus and to increase the spillway capacity of reservoir because the differences in the design capacities of reservoirs and corresponding maximum peak discharges (MPDs) are very little. The construction of dam in the vicinity of Jinnah Barrage is also of great importance provided all political issues are sorted out. The return period, corresponding to each value of risk, is found to be helpful in assessing what precautionary measures should be taken in case of flood according to risk value. For example, the flood risk at Tarbela Dam is 343,510 and 433,491 cfs against 2-and 4-year return period, respectively. Although both of these values lie in the range of medium flood, however, the difference in the values should reduce the amount of investment required in dealing with the expected flood. Acknowledgment We are thankful to Mr.

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Question: The 'Sukkur Dam' is on which river?
Answer: INDUS